## Warning: Removed 95 rows containing missing values (position_stack).
## Warning: Removed 95 rows containing missing values (position_stack).
##
## Pearson's product-moment correlation
##
## data: chuska_fd$anomaly_perc and chuska_fd$anomaly_pdsi
## t = 1.9047, df = 94, p-value = 0.05988
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.008029559 0.378620112
## sample estimates:
## cor
## 0.1927667
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## waterYear = col_integer(),
## swe_mm = col_double()
## )
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## pdsi = col_double()
## )
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## pdsi = col_double()
## )
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## pdsi = col_double()
## )
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## pdsi = col_double()
## )
Mid San Juan and Chuska have the highest correlation of winter swe and AMJ PDSI. Let’s check by year
##
## Call:
## lm(formula = tsaile_pdsi_mnth$pdsi ~ tsaile_pdsi_mnth$date)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1174 -1.3595 0.0247 1.4272 4.6402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.588e+00 2.181e-01 7.282 1.34e-12 ***
## tsaile_pdsi_mnth$date -1.714e-04 1.894e-05 -9.053 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.783 on 484 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1448, Adjusted R-squared: 0.143
## F-statistic: 81.95 on 1 and 484 DF, p-value: < 2.2e-16